森林小气候监测中无线传感器网络支撑技术的研究
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摘要
本文在无线传感器网络众多关键技术中,通过综合考虑森林环境和小气候监测的特点,自行设计了基于ZigBee/802.15.4标准无线传感器网络节点；改进定位算法、提高定位精度；在现有的基于RSSI能量优化算法的基础上,结合接收信号强度指示、链路质量指示这两个与信道质量有关的参数、以及节点间距离参数,构造基于能量的跨层优化算法,提高无线传感器网络的生命周期；同时建立传感器节点的数据融合机制,提高采集数据质量、降低节点间通讯的能量消耗,提高无线传感器网络的生命周期。
在围绕能量优化前提下,从物理层和MAC层入手设计了基于森林小气候监测的传感网功能结构和基于ZigBee/802.15.4标准的森林小气候监测传感网体系结构。根据森林小气候监测范围广、区域分散、地貌复杂的特点,设计了基于CC2520射频处理器和低功耗的MSP430F5437处理器的无线传感器网络节点以及结合ARM处理器和无线传感器网络节点的控制节点,在物理层、MAC上实现了针对森林小气候监测的无线传感器网络关键技术的研究。
为解决现有无线传感器网络定位算法复杂、定位精度低、计算能耗开销大的问题,结合研究对象的特点,对经典定位算法进行改进,设计了一种适用于大规模传感网的定位算法。该算法与经典MDS算法的MDS-MAP定位算法相比,计算复杂程度更低,仅使用节点间的一跳距离来计算簇内节点的距离,回避了节点间的最短路径估计,降低了能源消耗。该算法不仅可以在规则网络中获得较好的定位精度,而且在不规则网络中进行定位计算时,仍能获得较低的定位误差,该算法为跨层能量的设计提供了基本节点间的距离参数,同时为数据融和提供地理位置参考。
针对森林环境的特点,通过对无线电传播路径损耗模型以及CC2520的分析,建立了一种基于节点间距离、接收信号强度指示和链路质量指示的跨层能量优化算法。该算法适合在通信开销小、硬件要求低的场合下使用。用分段线性逼近的方法得到发送功率与接收的信号强度指示(RSSI)、链接质量指示(LQI)、节点间距离的关系。通过进一步优化得到发送功率与片码错误率、节点间距离的关系。实现了根据链路质量和节点间距离,在应用层、MAC层、物理层的跨层能量优化算法。通过在MATLAB上进行仿真,证明该算法较标准的数据发送方法在能量节省有明显的改善,该算法降低了能量消耗、减少了节点间碰撞,提高了传感网无线传感器网络的生命周期、延长了森林小气候监测周期。
在以上研究的基础上,根据森林小气候监测的数据特点,建立了可信度机制自适应与加权算法相结合的传感器节点数据融合机制,对单个节点内的数据进行融合、相邻节点间的同类数据进行融合以及控制器端不同簇间数据的融合,去除冗余数据,提高数据的准确度和数据采集效率,降低数据通讯的能量消耗,提高了传感网无线传感器网络的生命周期。
本文通过对上述内容的研究,结合森林小环境气候监测的特点,在节点硬件设计、定位算法改进、跨层节能算法、数据融和等几个无线传感器网络关键技术方面提出了具体实现方法,使无线传感器网络节点更加适合于森林小气候监测。
Through the comprehensive consideration of the characteristics forest environmental and microclimate monitoring in the wireless sensor networks, standard wireless sensor network node was designed on the basis of ZigBee/802.15.4standard, localization algorithm and accuracy were improved. Based on the present RSSI energy optimization algorithm and combined with the receiving signal strength and link quality instruction parameters, which had relationship with the channel quality, and the nodes distance parameter, energy cross layer optimization algorithm was constructed to advance the lifecycle of wireless sensor networks. The sensor data fusion mechanism was built, the data quality was improved, energy consumption of nodes communication was reduced and the lifecycle of wireless sensor networks was advanced.
On the basis of energy optimization, forest microclimate monitoring of sensor network function structure was designed from physical and MAC layer, and forest microclimate monitoring sensor networks structure was built based on ZigBee/802.15.4standard. According to the characteristics of the large forest microclimate monitoring range, regional geomorphic dispersion and complexity, wireless sensor network nodes and controlling nodes, which combined with ARM processor, were designed. The key technology in forest microclimate monitoring wireless sensor network was realized in the physical and MAC layer.
Considering the object of study, the classical localization algorithm was improved, and a kind of large-scale sensor network location algorithm was designed to solve localization algorithm complexity of the wireless sensor network, positioning accuracy and calculation costs low energy consumption. Compared to the MDS-MAP location algorithm of classic MDS algorithm, it has the advantages of low complexity, avoiding the shortest path estimation by using a jump to calculate cluster nodes in the distance, and reducing the energy consumption. The algorithm can not only gain better positioning accuracy at the regular networks, but also can gain the lower positioning error at the irregular networks. The algorithm provided the distance parameter between the basic nodes for the design of cross layer energy, and provided the geographical position reference of data fusion.
Considering the characteristic of forest environmental, a cross energy optimization algorithm was built on the basis of the nodes distance, strength instructions of receiving signal and link quality instruction by the analysis of radio wave propagation loss model and CC2520. This algorithm is suitable for the situations of low cost communication and low hardware requirement. The relevance between sending power and RSSI, LQI, the distance of the nodes was gained by piecewise linear approximation, which realized the energy optimization algorithm of cross layer among the application layer, the MAC layer and physical layer on the basis of link quality and the nodes distance. Simulation by MATLAB proved that there was obviously improvement in energy saving compare to standard data sending method. This algorithm reduced the energy consumption and the collision between the nodes, improving the sensor network of wireless sensor network life cycle and extending the monitoring cycle of forest microclimate.
On the basis of above studies and data characteristics of forest microclimate monitoring, data fusion mechanism of sensor nodes combined with self-adaptive and weighting algorithm of credibility mechanism was built for the fusion of single node, adjacent nodes of similar data and the data from different clusters. The data fusion mechanism can remove redundant data, improve the accuracy and collection efficiency of the data, reduce the energy consumption in the data communication and improve life cycle of the wireless sensor network.
In thesis, on the basis of above studies and data characteristics of forest microclimate monitoring, some key technologies in wireless sensor networks such as node hardware design, localization algorithm improving, cross layer energy-saving algorithm, data fusion etc. were realized, which made the wireless sensor network nodes fit for monitoring forest microclimate.
在围绕能量优化前提下,从物理层和MAC层入手设计了基于森林小气候监测的传感网功能结构和基于ZigBee/802.15.4标准的森林小气候监测传感网体系结构。根据森林小气候监测范围广、区域分散、地貌复杂的特点,设计了基于CC2520射频处理器和低功耗的MSP430F5437处理器的无线传感器网络节点以及结合ARM处理器和无线传感器网络节点的控制节点,在物理层、MAC上实现了针对森林小气候监测的无线传感器网络关键技术的研究。
为解决现有无线传感器网络定位算法复杂、定位精度低、计算能耗开销大的问题,结合研究对象的特点,对经典定位算法进行改进,设计了一种适用于大规模传感网的定位算法。该算法与经典MDS算法的MDS-MAP定位算法相比,计算复杂程度更低,仅使用节点间的一跳距离来计算簇内节点的距离,回避了节点间的最短路径估计,降低了能源消耗。该算法不仅可以在规则网络中获得较好的定位精度,而且在不规则网络中进行定位计算时,仍能获得较低的定位误差,该算法为跨层能量的设计提供了基本节点间的距离参数,同时为数据融和提供地理位置参考。
针对森林环境的特点,通过对无线电传播路径损耗模型以及CC2520的分析,建立了一种基于节点间距离、接收信号强度指示和链路质量指示的跨层能量优化算法。该算法适合在通信开销小、硬件要求低的场合下使用。用分段线性逼近的方法得到发送功率与接收的信号强度指示(RSSI)、链接质量指示(LQI)、节点间距离的关系。通过进一步优化得到发送功率与片码错误率、节点间距离的关系。实现了根据链路质量和节点间距离,在应用层、MAC层、物理层的跨层能量优化算法。通过在MATLAB上进行仿真,证明该算法较标准的数据发送方法在能量节省有明显的改善,该算法降低了能量消耗、减少了节点间碰撞,提高了传感网无线传感器网络的生命周期、延长了森林小气候监测周期。
在以上研究的基础上,根据森林小气候监测的数据特点,建立了可信度机制自适应与加权算法相结合的传感器节点数据融合机制,对单个节点内的数据进行融合、相邻节点间的同类数据进行融合以及控制器端不同簇间数据的融合,去除冗余数据,提高数据的准确度和数据采集效率,降低数据通讯的能量消耗,提高了传感网无线传感器网络的生命周期。
本文通过对上述内容的研究,结合森林小环境气候监测的特点,在节点硬件设计、定位算法改进、跨层节能算法、数据融和等几个无线传感器网络关键技术方面提出了具体实现方法,使无线传感器网络节点更加适合于森林小气候监测。
Through the comprehensive consideration of the characteristics forest environmental and microclimate monitoring in the wireless sensor networks, standard wireless sensor network node was designed on the basis of ZigBee/802.15.4standard, localization algorithm and accuracy were improved. Based on the present RSSI energy optimization algorithm and combined with the receiving signal strength and link quality instruction parameters, which had relationship with the channel quality, and the nodes distance parameter, energy cross layer optimization algorithm was constructed to advance the lifecycle of wireless sensor networks. The sensor data fusion mechanism was built, the data quality was improved, energy consumption of nodes communication was reduced and the lifecycle of wireless sensor networks was advanced.
On the basis of energy optimization, forest microclimate monitoring of sensor network function structure was designed from physical and MAC layer, and forest microclimate monitoring sensor networks structure was built based on ZigBee/802.15.4standard. According to the characteristics of the large forest microclimate monitoring range, regional geomorphic dispersion and complexity, wireless sensor network nodes and controlling nodes, which combined with ARM processor, were designed. The key technology in forest microclimate monitoring wireless sensor network was realized in the physical and MAC layer.
Considering the object of study, the classical localization algorithm was improved, and a kind of large-scale sensor network location algorithm was designed to solve localization algorithm complexity of the wireless sensor network, positioning accuracy and calculation costs low energy consumption. Compared to the MDS-MAP location algorithm of classic MDS algorithm, it has the advantages of low complexity, avoiding the shortest path estimation by using a jump to calculate cluster nodes in the distance, and reducing the energy consumption. The algorithm can not only gain better positioning accuracy at the regular networks, but also can gain the lower positioning error at the irregular networks. The algorithm provided the distance parameter between the basic nodes for the design of cross layer energy, and provided the geographical position reference of data fusion.
Considering the characteristic of forest environmental, a cross energy optimization algorithm was built on the basis of the nodes distance, strength instructions of receiving signal and link quality instruction by the analysis of radio wave propagation loss model and CC2520. This algorithm is suitable for the situations of low cost communication and low hardware requirement. The relevance between sending power and RSSI, LQI, the distance of the nodes was gained by piecewise linear approximation, which realized the energy optimization algorithm of cross layer among the application layer, the MAC layer and physical layer on the basis of link quality and the nodes distance. Simulation by MATLAB proved that there was obviously improvement in energy saving compare to standard data sending method. This algorithm reduced the energy consumption and the collision between the nodes, improving the sensor network of wireless sensor network life cycle and extending the monitoring cycle of forest microclimate.
On the basis of above studies and data characteristics of forest microclimate monitoring, data fusion mechanism of sensor nodes combined with self-adaptive and weighting algorithm of credibility mechanism was built for the fusion of single node, adjacent nodes of similar data and the data from different clusters. The data fusion mechanism can remove redundant data, improve the accuracy and collection efficiency of the data, reduce the energy consumption in the data communication and improve life cycle of the wireless sensor network.
In thesis, on the basis of above studies and data characteristics of forest microclimate monitoring, some key technologies in wireless sensor networks such as node hardware design, localization algorithm improving, cross layer energy-saving algorithm, data fusion etc. were realized, which made the wireless sensor network nodes fit for monitoring forest microclimate.
引文
[1]陈宏志,胡庭兴,龚伟,王景燕,肖波.我国森林小气候的研究现状[J].四川林业科技,2007,28(2)：28-32
[2]张璐,林伟强.森林小气候观测研究概述[J].广东林业科技,2002,18(4)：52-55
[3]崔莉,鞠海玲,苗勇,李天璞,刘巍,赵泽.无线传感器网络研究进展[J].计算机研究与发展,2005,42(1)：162-174
[4]马祖长,孙怡宁,梅涛.无线传感器网络综述[J].通信学报,2004,25(4)：114-122
[5]刘刚,周兴社,谷建华,李志刚.自组织、自适应无线传感器网络理论研究[J].计算机应用研究,2005(5)：30-33
[6]Akyildiz I F, Su W, Sankarasubramaniam et al[J]. Wireless sensor network:A survey.Computer Networks,2003,38(4):393-422
[7]Yu Y, Govindan R, Estrin D. Geographical and Energy Aware Routing:A Recursive Data Dissemination Protocol for Wireless Sensor Networks[R].UCLA Computer Science Department Technical Report,2001
[8]刘永强,郑宾.用于环境监测的无线传感器网络节点设计[J].电子测试,2007(11)：49-51
[9]纪金水.ZigBee无线传感器网络技术在工业自动化监测中的应用[J].工业仪表与自动化装置,2007(3)：71-76
[10]眭碧霞.无线传感器网络环境监测系统[J].常州信息职业技术学院学报,2007,6(3)：7-8
[11]HeinzelmanW R, Chandrakasan A, Bal Krishnan H. Energy Efficient Communication Protocol for Wireless Microsensor Networks [C]. Proceedings of the 33 rd Hawaii International Conference on System Sciences,2000
[12]李晓维.无线传感器网络技术[M].北京：北京理工大学出版社,2007
[13]焦正,吴明红,王德庆.分布式传感器网络研究进展[J].传感器世界,2004(10)：6-9
[14]黄琦,井实,杨江,张杨.一种用于电力系统广域测量的传感器网络设计与实现[C].中国电机工程学会第十届青年学术会议.吉林市,2008
[15]刘劲风,王述阳WAMS在电力系统分析和控制中应用的新进展[J].高电压技术,2007(7)：182-184
[16]孙姬,陈霞,谈振辉.无线传感器网络路由技术浅析[J].传感器世界,2005(11)：30-34
[17]TinyOS[OL].http://tinyos.millennium.berkeley.edu/
[18]TinyDB[OL].http://telegraph.cs.berkeley.edu/tinydb/
[19]曹涌涛.无线传感器网络自组织算法关键技术的研究[D].上海：上海交通大学, 2006
[20]李建中,李金宝,石胜飞.传感器网络及其数据管理的概念、问题与进展[J].软件学报.2003,14(10)：1717-1725
[21]中国科学院计算技术研究所传感器网络实验室[OL].http://www.easinet.cn/
[22]无线传感器基础理论及关键技术研究[OL].http://wsn.973program.org/
[23]郑国强,李建中,周志立.无线传感器网络MAC协议研究进展[J].自动化学报,2008(34)：305-316
[24]浙江大学网络传感与控制研究组[OL].http://www.sensornet.cn/
[25]李生辰.无线传感器网络在智能家居系统中的应用[D].辽宁工学院,2007
[26]耿军涛,周小佳.一种基于无线传感器网络大气环境监测系统[J].传感器世界,2007(6)：30-33
[27]魏书芳.智能无线瓦斯传感器的研制[D].山东大学,2007
[28]王超楠,丁恩杰,周强,徐勇军,李晓维.应用无线传感器网络快速定位矿井配电网故障的方法[J].计算机辅助设计与图形学学报,2007,19(3)：392-396
[29]殷悦,袁慎芳,吴键,丁建伟,尚盈.基于无线传感器网络的远距离结构健康监测[J].传感器与微系统,2007,26(6)：33-38
[30]Walrod J.Sensor Network Technology for Joint Under Sea Warfare[C].In proceeding of the NDIA Joint Under Sea Warfare Technology Conference,San Diego,2002:246-254
[31]Arici T,Altunbasak Y.Acative Sensing for Environment Monitoring Using Wireless Sensor Networks [C].In Proceeding of the IEEE Wireless Communications and Networking Conference(WCNC),2004,5(1):2350-2355
[32]ALERT[OL].Flood Warning Technologies,2003.http://www.altersystem.org.
[33]Main waring A,Polastre J.Wireless Sensor Networks for Habitat Monitoring[C].In Proceeding of the ACMWSNA,2002(2):88-97
[34]Arici T,Altunbasak Y.Acative Sensing for Environment Monitoring Using Wireless Sensor Networks [C].In Proceeding of the IEEE Wireless Communications and Networking Conference(WCNC),2004,5(1):2350-2355
[35]ALERT[OL].Flood Warning Technologies,2003.http://www.altersystem.org.
[36]Mainwaring A,Polastre J.Wireless Sensor Networks for Habitat Monitoring[C].In Proceeding of the ACMWSNA,2002(2):88-97
[37]侯惠峰.无线传感器网络寿命的一种新定义方法.计算机工程与应用.2005(4)：29-31
[38]Kulik J, Heinzelman W R, et al. Negotiation-based protocols for disseminating information in wireless sensor networks.Wireless Networks,2002,8(8):169-185
[39]Ning Li and Jennifer C.Hou. Topology Control in Heterogeneous Wireless Networks:Problems and Solutions.in Proceedings of IEEE Infocom 2004
[40]Yan Gao,Jennifer C.Hou and Hoang Nguyen.Topology Control for Maintaining Network Connectivity and Maximzing Network Capacity Under the Physical Model.in Proceedings of IEEE Infocom 2008
[41]GUTIERREZ J A.IEEE Standard for Part15.4:Wireless Medium Access Control(MAC) and Physical Layer(PHY) specifications for Low Rate Wireless Personal Area Networks(LR2W PAN)[S],2003
[42]Rabaey MJ,AnmterM,Silva LJ.Pico Radio Supports ad hoc ultralOw Power wireless networking[J].IEEE Computer Magazine,2000,33(7):42-48
[43]POWERS RA.Batteries for low power electronics[J].Proc.IEEE,1995,83(4):687-693
[44]ShijinD, Xiaorong J, Lemin L. Research and Analysis on Routing Protocols for Wireless Sensor Networks [C]. International Conference on Communications, Circuits and Systems Proceedings,2005,407-411
[45]L.M.Feeney,M.Nilson.Investigating the Energy Consumption of a Wireless Network Interface in an Ad Hoc Networking Environment.in Proceedings of IEEE Ifocom 2001
[46]W.Heinzelman,A.Chandrakasan,H.Balakrishnan.Energy efficiente communication Protocol for wireless microsensor networks.in Proceedings of the 33rd Intenational Conference on System Scienees,January 2000
[47]翟明岳,林靖颖,孙凤杰,卢文冰,唐良瑞Adhoc网络在配电自动化系统中的应用及其连接概率分析[J].电气应用,2005,24(1)：14-17
[48]Ron Banner and Ariel Orda.Multi-Objective To Pology Control in Wireless Networks.in Proceedings of IEEE Infocom 2008
[49]Kannan Srinivasan and PhiliP Levis.RSSI is Under Appreeiated.in the Third Workshop on Embedded Networked Sensors(EmNets2006),2006(5)
[50]凌志浩,周怡颁,郑丽国ZigBee无线通信技术及其应用研究[J].华东理工大学学报：自然科学版,2006,32(7)：801-805
[51]HIGHTOWER J, BORIELLO G,WANTR. SpotON.An Indoor3D Location Sensing Technology Based on RF Signal Strength[R].Technical Report UW CSE00-02-02,University of Washington,Department of Computer Science and Engineering,Seattle,WA,USA.2000
[52]Cheng M X,Cardei M,Jinhua Sun,et al. Topology control of ad hoc wireless networks for energy efficiency[J].IEEE transactions on computers,2004,53 (12):1629-1635
[53]HARDER A,SONG L,WANG Y. Towards an Indoor Location Sys-tem Using RF Signal Strength in IEEE802.11Networks[C].Pro-ceedings of the International Conference on Information Technology:Coding and Computing(ITCC05).2005:228-233
[54]孙利民,李建中,陈渝,等.无线传感器网络[M].北京：清华大学出版社,2005
[55]郑英.无线传感器网络技术在林火监测中的应用[J].森林防火,2009(4)：36-39
[56]刘劲风.无线传感器网络在森林监测中的应用.林业科技[J].2010,35(4)：18-20
[57]马永强,李静强,冯立营.基于ZigBee技术的射频芯片CC2430[J].单片机与嵌入式系统,2006(3)：45-47
[58]王海东,孙利民.无线传感器网络的定位机制[J].计算机科学,2006,33(4)：36-38
[59]Cohen, David.ZigBee help the future.Electronic Products(Garden City, New York),June,2009
[60]Akyildiz I F, Su W, et al. A Survey on Sensor Networks [J]. IEEE Communications Magazine,2002,40(8):102-114
[61]Mainwaring A,Polastre J,Szewczyk R,et al. Wireless senor networks for habitat monitoring[C].ACM international workshop onwireless sensor networks and applications. 2002
[62]MSP430x5xx Family User's Guide[EB/OL]. http://www.ti.com/files/Msp430543x.pdf
[63]Intanagonwiwat C, Govindan R, Estrin D.Directed Diffusion:A Scalable and Robust Communication Paradigm for Sensor Networks[C].Proceedings of the 6th Annual ACM/ IEEE International Conference on Mobile Computing and Networking (MOB ICOM'00), 2000:56-67
[64]王福豹,史龙,任丰原.无线传感器网络中的自身定位系统和算法[J].软件学报,2005,16(5)：857-868
[65]马祖长,孙怡宁.无线传感器网络节点的定位算法[J].计算机工程,2004,30(7)：13-14
[66]PRIYANTHA NB, ANIT C, HARI B. The cricket location-support system[C].The6th ACM/IEEE International Conference on Mobile Computing and Networking Proceedings of the6th Annual ACM In-ternational Conference on Mobile Computing and Networking(Mobi-Com'00). ACMPress,2000:32-43
[67]Sehwiebert L,Sandeep K,Gupta S.Research Challenges in Wireless Networks of Biomedieal Sensors[C].In Proceeding of the 7th Annual International Conference on Mobile Computing and Networking,2001:151-165
[68]石为人,许磊.一种面向无线传感器网络相对定位的分簇算法[J].计算机工程与应用,2008,44(24)：15-18
[69]冯贤文,于宁.基于多维定标的无线传感器网络定位算法研究[J].仪器仪表标准化与计量,2007(6)：13-16
[70]Costa J A,Patwari N.Distributed Weighted Multi Dimensional Scaling for Node Localization in Sensor Networks [J]. ACM Transactions on Sensor Networks Journal, 2006,2(l):39-64
[71]Ji Xiang.Sensor positioning in wireless ad-hoc sensor networks using multi Dimensional scaling[EB/OL].2004. http://www.ieeeinfocom.org/2004/Papers
[72]Vijayanth Vivekanandan, Vincent W.S.Ordinal MDS-based Localization for Wireless Sensor Networks [J]. International Journal of Sensor Networks,2006(1):169-178
[73]肖玲,李仁发,罗娟.基于非度量多维标度的无线传感器网络节点定位算法[J].计算机研究与发展,2007,44(3)：399-405
[74]彭鑫,李仁发,罗娟.一种基于非度量多维标度的移动定位算法[J].计算机科学,2008,35(10)：219-222
[75]王健,赵亚凤,刘劲风.一种适用于大规模无线传感器网络的定位算法[J].东北林业大学学报,2009,37(8)：84-89
[76]黎宁,史诚光,谢胜利.无线局域网能耗性能分析[J].华南理工大学学报(自然科学版),2006,34(9)：8-11
[77]朱剑,赵海,徐久强,张希元,陆育慧.WSN中基于跨层策略的低能耗/高可靠通信问题研究[J].系统仿真学报.2009,21(16)：4975-4979
[78]孙佩刚,赵海,罗玎玎等.无线传感器网络链路通信质量测量研究[J].通信学报,2007,28(10)：14-22
[79]张大踪,杨涛等.无线传感器网络的功率控制[J].传感器世界,2007,(1)：34-37
[80]NARPC,CAYIRCIE.PCSMAC:A Power Controlled Sensor MAC Protocol for Wireless Sensor Networks[C].Proc of IEEE CNF,2005:81-92
[81]黄顺炎,杨震,胡海峰.基于功率控制的IEEE 802.15.4算法在无线传感器网络中的应用[J].南京邮电大学学报,2006,26(5)：41-45
[82]郑少仁,王海涛,赵志峰等AdHoC网络技术[M].北京：人民邮电出版社,2005
[83]Kawadia V,Kumar PR.Principles and Protocols for Power control in wireless ad hoc networks.IEEE Journal on Selected Areas in Communications,2005,23(1):76-88.
[84]Mohamed R,Fahmy S,Pandurangan G.Latency Sensitive Power control for wireless ad hoc networks.In:BoukereheA,ed.Proc.of the MSWIM2005,Montreal:ACM Press,2005:31-38.
[85]李方敏,徐文君,刘新华.无线传感器网络功率控制技术[J].软件学报,2008,19(3)：717-718
[86]Chipcon.SmartRF CC2520:2.4 GHZ IEEE 802.15.4/ZIGBEE(?) RF TRANSCEIVER [EB/OL].http://www.chipcon.com/files/CC2520 DataSheet.pdf
[87]IEEE Std 802.15.4-2003.Standard for telecommunications and information exchange between system-local area medium access control(MAC) and physical layer(PHY) specifications for low rate wireless personal area networks(WPAN).2003
[88]ZigBee Alliance.ZigBee Document 053474r06.Version 1.0[C].December 14th,2004
[89]陈维克,李文锋,袁兵等.基于RSSI的无线传感器网络加权质心定位算法[J].武汉理工大学学报(交通科学与工程版),2006,30(2)：265-268
[90]KANNAN R,KAL ID IND I R,IYENGAR S.Energy and rate based MAC protocol for wireless sensor networks[J].ACM Sigmod Record,2003,32(4):60-65
[91]ZigBee Technical Overview,ZigBee Alliance,May,2007
[92]杨露,邹岗.多传感器分布式融合检测自适应算法[J].探测与控制学报, 2006,28(5)：28-30
[93]胡丹丹,肖书明.基于多传感器的数据融合技术[J].东北电力学院学报,2004,24(1)：62-66
[94]Sumi,M.Design of a ZigBee-based RF,ID network for industry applications,Proceedings of the 2nd International Conference on Security of Information and Networks,2009:111-116
[95]Li Heng.The application of LZSS in the RFID tags.Proceedings 1st International Symposiumon ComPuter Network and Multi media Technology,2009
[96]Wiseman,Yair. Data Science Journal.The relative efficiency of data compression by LZW and LZSS.DataScieneeJoumal.2007
[97]郑勇,杨志义,李志刚.基于无线传感器网络的网内数据融合[J].计算应用研究,2004(4)：243-245
[98]杨万海.多传感器数据融合及其应用(第一版)[M].西安：西安电子科技大学出版社,2004
[99]张建明,宋迎清,周四望.无线传感器网络中数据汇聚技术的研究[J].计算机应用,2006,26(6)：1273-1278
[100]廖惜春,,丘敏,麦汉荣.基于参数估计的数据融合算法研究[J].传感器与微系统,2006,25(10)：70-73
[101]付华,杜晓坤.基于Bayes估计理论的数据融合方法[J].自动化技术与应用,2005,24(4)：10-12
[102]张西良,孙优.无线传感器网络基于定向扩散与分批估计的数据融合算法[J].传感器与仪器仪表,2006,22(9-1)：173-175
[103]宿陆,李全龙,徐晓飞.基于D-S证据理论的传感器网络数据融合算法[J].小型微型计算机系统,2006,27(7)：1321-1325
[104]滕召胜,罗隆福.智能检测系统与数据融合(第一版)[M].北京：机械工业出版社,2002
[105]Kong F-T,Chen Y-P,Xie J-M,et al.Distributed temperature control system based on multi-sensor data fusion[C].in.Guangzhou,China:Institute of Electrical and Electronics Engineers Computer Society,Piscataway,NJ 08855-1331,United States,2005:494-498
[106]Yotmis and S.Fahmy.Heed:A hybrid,energy-efficient,distributed clustering approach for ad-hoc sensornetworks.IEEE Transactions on Mobile Computing,vol.3,no.4,PP.366-379,October-December 2004
[107]Wang Shu, Liu Kelong.A Distributed Sensor Network Localization Scheme Motivated by Graph Rigidity Theory[J].The Mediterranean Journal of Measurement and Control,2005,(4):185-190
[2]张璐,林伟强.森林小气候观测研究概述[J].广东林业科技,2002,18(4)：52-55
[3]崔莉,鞠海玲,苗勇,李天璞,刘巍,赵泽.无线传感器网络研究进展[J].计算机研究与发展,2005,42(1)：162-174
[4]马祖长,孙怡宁,梅涛.无线传感器网络综述[J].通信学报,2004,25(4)：114-122
[5]刘刚,周兴社,谷建华,李志刚.自组织、自适应无线传感器网络理论研究[J].计算机应用研究,2005(5)：30-33
[6]Akyildiz I F, Su W, Sankarasubramaniam et al[J]. Wireless sensor network:A survey.Computer Networks,2003,38(4):393-422
[7]Yu Y, Govindan R, Estrin D. Geographical and Energy Aware Routing:A Recursive Data Dissemination Protocol for Wireless Sensor Networks[R].UCLA Computer Science Department Technical Report,2001
[8]刘永强,郑宾.用于环境监测的无线传感器网络节点设计[J].电子测试,2007(11)：49-51
[9]纪金水.ZigBee无线传感器网络技术在工业自动化监测中的应用[J].工业仪表与自动化装置,2007(3)：71-76
[10]眭碧霞.无线传感器网络环境监测系统[J].常州信息职业技术学院学报,2007,6(3)：7-8
[11]HeinzelmanW R, Chandrakasan A, Bal Krishnan H. Energy Efficient Communication Protocol for Wireless Microsensor Networks [C]. Proceedings of the 33 rd Hawaii International Conference on System Sciences,2000
[12]李晓维.无线传感器网络技术[M].北京：北京理工大学出版社,2007
[13]焦正,吴明红,王德庆.分布式传感器网络研究进展[J].传感器世界,2004(10)：6-9
[14]黄琦,井实,杨江,张杨.一种用于电力系统广域测量的传感器网络设计与实现[C].中国电机工程学会第十届青年学术会议.吉林市,2008
[15]刘劲风,王述阳WAMS在电力系统分析和控制中应用的新进展[J].高电压技术,2007(7)：182-184
[16]孙姬,陈霞,谈振辉.无线传感器网络路由技术浅析[J].传感器世界,2005(11)：30-34
[17]TinyOS[OL].http://tinyos.millennium.berkeley.edu/
[18]TinyDB[OL].http://telegraph.cs.berkeley.edu/tinydb/
[19]曹涌涛.无线传感器网络自组织算法关键技术的研究[D].上海：上海交通大学, 2006
[20]李建中,李金宝,石胜飞.传感器网络及其数据管理的概念、问题与进展[J].软件学报.2003,14(10)：1717-1725
[21]中国科学院计算技术研究所传感器网络实验室[OL].http://www.easinet.cn/
[22]无线传感器基础理论及关键技术研究[OL].http://wsn.973program.org/
[23]郑国强,李建中,周志立.无线传感器网络MAC协议研究进展[J].自动化学报,2008(34)：305-316
[24]浙江大学网络传感与控制研究组[OL].http://www.sensornet.cn/
[25]李生辰.无线传感器网络在智能家居系统中的应用[D].辽宁工学院,2007
[26]耿军涛,周小佳.一种基于无线传感器网络大气环境监测系统[J].传感器世界,2007(6)：30-33
[27]魏书芳.智能无线瓦斯传感器的研制[D].山东大学,2007
[28]王超楠,丁恩杰,周强,徐勇军,李晓维.应用无线传感器网络快速定位矿井配电网故障的方法[J].计算机辅助设计与图形学学报,2007,19(3)：392-396
[29]殷悦,袁慎芳,吴键,丁建伟,尚盈.基于无线传感器网络的远距离结构健康监测[J].传感器与微系统,2007,26(6)：33-38
[30]Walrod J.Sensor Network Technology for Joint Under Sea Warfare[C].In proceeding of the NDIA Joint Under Sea Warfare Technology Conference,San Diego,2002:246-254
[31]Arici T,Altunbasak Y.Acative Sensing for Environment Monitoring Using Wireless Sensor Networks [C].In Proceeding of the IEEE Wireless Communications and Networking Conference(WCNC),2004,5(1):2350-2355
[32]ALERT[OL].Flood Warning Technologies,2003.http://www.altersystem.org.
[33]Main waring A,Polastre J.Wireless Sensor Networks for Habitat Monitoring[C].In Proceeding of the ACMWSNA,2002(2):88-97
[34]Arici T,Altunbasak Y.Acative Sensing for Environment Monitoring Using Wireless Sensor Networks [C].In Proceeding of the IEEE Wireless Communications and Networking Conference(WCNC),2004,5(1):2350-2355
[35]ALERT[OL].Flood Warning Technologies,2003.http://www.altersystem.org.
[36]Mainwaring A,Polastre J.Wireless Sensor Networks for Habitat Monitoring[C].In Proceeding of the ACMWSNA,2002(2):88-97
[37]侯惠峰.无线传感器网络寿命的一种新定义方法.计算机工程与应用.2005(4)：29-31
[38]Kulik J, Heinzelman W R, et al. Negotiation-based protocols for disseminating information in wireless sensor networks.Wireless Networks,2002,8(8):169-185
[39]Ning Li and Jennifer C.Hou. Topology Control in Heterogeneous Wireless Networks:Problems and Solutions.in Proceedings of IEEE Infocom 2004
[40]Yan Gao,Jennifer C.Hou and Hoang Nguyen.Topology Control for Maintaining Network Connectivity and Maximzing Network Capacity Under the Physical Model.in Proceedings of IEEE Infocom 2008
[41]GUTIERREZ J A.IEEE Standard for Part15.4:Wireless Medium Access Control(MAC) and Physical Layer(PHY) specifications for Low Rate Wireless Personal Area Networks(LR2W PAN)[S],2003
[42]Rabaey MJ,AnmterM,Silva LJ.Pico Radio Supports ad hoc ultralOw Power wireless networking[J].IEEE Computer Magazine,2000,33(7):42-48
[43]POWERS RA.Batteries for low power electronics[J].Proc.IEEE,1995,83(4):687-693
[44]ShijinD, Xiaorong J, Lemin L. Research and Analysis on Routing Protocols for Wireless Sensor Networks [C]. International Conference on Communications, Circuits and Systems Proceedings,2005,407-411
[45]L.M.Feeney,M.Nilson.Investigating the Energy Consumption of a Wireless Network Interface in an Ad Hoc Networking Environment.in Proceedings of IEEE Ifocom 2001
[46]W.Heinzelman,A.Chandrakasan,H.Balakrishnan.Energy efficiente communication Protocol for wireless microsensor networks.in Proceedings of the 33rd Intenational Conference on System Scienees,January 2000
[47]翟明岳,林靖颖,孙凤杰,卢文冰,唐良瑞Adhoc网络在配电自动化系统中的应用及其连接概率分析[J].电气应用,2005,24(1)：14-17
[48]Ron Banner and Ariel Orda.Multi-Objective To Pology Control in Wireless Networks.in Proceedings of IEEE Infocom 2008
[49]Kannan Srinivasan and PhiliP Levis.RSSI is Under Appreeiated.in the Third Workshop on Embedded Networked Sensors(EmNets2006),2006(5)
[50]凌志浩,周怡颁,郑丽国ZigBee无线通信技术及其应用研究[J].华东理工大学学报：自然科学版,2006,32(7)：801-805
[51]HIGHTOWER J, BORIELLO G,WANTR. SpotON.An Indoor3D Location Sensing Technology Based on RF Signal Strength[R].Technical Report UW CSE00-02-02,University of Washington,Department of Computer Science and Engineering,Seattle,WA,USA.2000
[52]Cheng M X,Cardei M,Jinhua Sun,et al. Topology control of ad hoc wireless networks for energy efficiency[J].IEEE transactions on computers,2004,53 (12):1629-1635
[53]HARDER A,SONG L,WANG Y. Towards an Indoor Location Sys-tem Using RF Signal Strength in IEEE802.11Networks[C].Pro-ceedings of the International Conference on Information Technology:Coding and Computing(ITCC05).2005:228-233
[54]孙利民,李建中,陈渝,等.无线传感器网络[M].北京：清华大学出版社,2005
[55]郑英.无线传感器网络技术在林火监测中的应用[J].森林防火,2009(4)：36-39
[56]刘劲风.无线传感器网络在森林监测中的应用.林业科技[J].2010,35(4)：18-20
[57]马永强,李静强,冯立营.基于ZigBee技术的射频芯片CC2430[J].单片机与嵌入式系统,2006(3)：45-47
[58]王海东,孙利民.无线传感器网络的定位机制[J].计算机科学,2006,33(4)：36-38
[59]Cohen, David.ZigBee help the future.Electronic Products(Garden City, New York),June,2009
[60]Akyildiz I F, Su W, et al. A Survey on Sensor Networks [J]. IEEE Communications Magazine,2002,40(8):102-114
[61]Mainwaring A,Polastre J,Szewczyk R,et al. Wireless senor networks for habitat monitoring[C].ACM international workshop onwireless sensor networks and applications. 2002
[62]MSP430x5xx Family User's Guide[EB/OL]. http://www.ti.com/files/Msp430543x.pdf
[63]Intanagonwiwat C, Govindan R, Estrin D.Directed Diffusion:A Scalable and Robust Communication Paradigm for Sensor Networks[C].Proceedings of the 6th Annual ACM/ IEEE International Conference on Mobile Computing and Networking (MOB ICOM'00), 2000:56-67
[64]王福豹,史龙,任丰原.无线传感器网络中的自身定位系统和算法[J].软件学报,2005,16(5)：857-868
[65]马祖长,孙怡宁.无线传感器网络节点的定位算法[J].计算机工程,2004,30(7)：13-14
[66]PRIYANTHA NB, ANIT C, HARI B. The cricket location-support system[C].The6th ACM/IEEE International Conference on Mobile Computing and Networking Proceedings of the6th Annual ACM In-ternational Conference on Mobile Computing and Networking(Mobi-Com'00). ACMPress,2000:32-43
[67]Sehwiebert L,Sandeep K,Gupta S.Research Challenges in Wireless Networks of Biomedieal Sensors[C].In Proceeding of the 7th Annual International Conference on Mobile Computing and Networking,2001:151-165
[68]石为人,许磊.一种面向无线传感器网络相对定位的分簇算法[J].计算机工程与应用,2008,44(24)：15-18
[69]冯贤文,于宁.基于多维定标的无线传感器网络定位算法研究[J].仪器仪表标准化与计量,2007(6)：13-16
[70]Costa J A,Patwari N.Distributed Weighted Multi Dimensional Scaling for Node Localization in Sensor Networks [J]. ACM Transactions on Sensor Networks Journal, 2006,2(l):39-64
[71]Ji Xiang.Sensor positioning in wireless ad-hoc sensor networks using multi Dimensional scaling[EB/OL].2004. http://www.ieeeinfocom.org/2004/Papers
[72]Vijayanth Vivekanandan, Vincent W.S.Ordinal MDS-based Localization for Wireless Sensor Networks [J]. International Journal of Sensor Networks,2006(1):169-178
[73]肖玲,李仁发,罗娟.基于非度量多维标度的无线传感器网络节点定位算法[J].计算机研究与发展,2007,44(3)：399-405
[74]彭鑫,李仁发,罗娟.一种基于非度量多维标度的移动定位算法[J].计算机科学,2008,35(10)：219-222
[75]王健,赵亚凤,刘劲风.一种适用于大规模无线传感器网络的定位算法[J].东北林业大学学报,2009,37(8)：84-89
[76]黎宁,史诚光,谢胜利.无线局域网能耗性能分析[J].华南理工大学学报(自然科学版),2006,34(9)：8-11
[77]朱剑,赵海,徐久强,张希元,陆育慧.WSN中基于跨层策略的低能耗/高可靠通信问题研究[J].系统仿真学报.2009,21(16)：4975-4979
[78]孙佩刚,赵海,罗玎玎等.无线传感器网络链路通信质量测量研究[J].通信学报,2007,28(10)：14-22
[79]张大踪,杨涛等.无线传感器网络的功率控制[J].传感器世界,2007,(1)：34-37
[80]NARPC,CAYIRCIE.PCSMAC:A Power Controlled Sensor MAC Protocol for Wireless Sensor Networks[C].Proc of IEEE CNF,2005:81-92
[81]黄顺炎,杨震,胡海峰.基于功率控制的IEEE 802.15.4算法在无线传感器网络中的应用[J].南京邮电大学学报,2006,26(5)：41-45
[82]郑少仁,王海涛,赵志峰等AdHoC网络技术[M].北京：人民邮电出版社,2005
[83]Kawadia V,Kumar PR.Principles and Protocols for Power control in wireless ad hoc networks.IEEE Journal on Selected Areas in Communications,2005,23(1):76-88.
[84]Mohamed R,Fahmy S,Pandurangan G.Latency Sensitive Power control for wireless ad hoc networks.In:BoukereheA,ed.Proc.of the MSWIM2005,Montreal:ACM Press,2005:31-38.
[85]李方敏,徐文君,刘新华.无线传感器网络功率控制技术[J].软件学报,2008,19(3)：717-718
[86]Chipcon.SmartRF CC2520:2.4 GHZ IEEE 802.15.4/ZIGBEE(?) RF TRANSCEIVER [EB/OL].http://www.chipcon.com/files/CC2520 DataSheet.pdf
[87]IEEE Std 802.15.4-2003.Standard for telecommunications and information exchange between system-local area medium access control(MAC) and physical layer(PHY) specifications for low rate wireless personal area networks(WPAN).2003
[88]ZigBee Alliance.ZigBee Document 053474r06.Version 1.0[C].December 14th,2004
[89]陈维克,李文锋,袁兵等.基于RSSI的无线传感器网络加权质心定位算法[J].武汉理工大学学报(交通科学与工程版),2006,30(2)：265-268
[90]KANNAN R,KAL ID IND I R,IYENGAR S.Energy and rate based MAC protocol for wireless sensor networks[J].ACM Sigmod Record,2003,32(4):60-65
[91]ZigBee Technical Overview,ZigBee Alliance,May,2007
[92]杨露,邹岗.多传感器分布式融合检测自适应算法[J].探测与控制学报, 2006,28(5)：28-30
[93]胡丹丹,肖书明.基于多传感器的数据融合技术[J].东北电力学院学报,2004,24(1)：62-66
[94]Sumi,M.Design of a ZigBee-based RF,ID network for industry applications,Proceedings of the 2nd International Conference on Security of Information and Networks,2009:111-116
[95]Li Heng.The application of LZSS in the RFID tags.Proceedings 1st International Symposiumon ComPuter Network and Multi media Technology,2009
[96]Wiseman,Yair. Data Science Journal.The relative efficiency of data compression by LZW and LZSS.DataScieneeJoumal.2007
[97]郑勇,杨志义,李志刚.基于无线传感器网络的网内数据融合[J].计算应用研究,2004(4)：243-245
[98]杨万海.多传感器数据融合及其应用(第一版)[M].西安：西安电子科技大学出版社,2004
[99]张建明,宋迎清,周四望.无线传感器网络中数据汇聚技术的研究[J].计算机应用,2006,26(6)：1273-1278
[100]廖惜春,,丘敏,麦汉荣.基于参数估计的数据融合算法研究[J].传感器与微系统,2006,25(10)：70-73
[101]付华,杜晓坤.基于Bayes估计理论的数据融合方法[J].自动化技术与应用,2005,24(4)：10-12
[102]张西良,孙优.无线传感器网络基于定向扩散与分批估计的数据融合算法[J].传感器与仪器仪表,2006,22(9-1)：173-175
[103]宿陆,李全龙,徐晓飞.基于D-S证据理论的传感器网络数据融合算法[J].小型微型计算机系统,2006,27(7)：1321-1325
[104]滕召胜,罗隆福.智能检测系统与数据融合(第一版)[M].北京：机械工业出版社,2002
[105]Kong F-T,Chen Y-P,Xie J-M,et al.Distributed temperature control system based on multi-sensor data fusion[C].in.Guangzhou,China:Institute of Electrical and Electronics Engineers Computer Society,Piscataway,NJ 08855-1331,United States,2005:494-498
[106]Yotmis and S.Fahmy.Heed:A hybrid,energy-efficient,distributed clustering approach for ad-hoc sensornetworks.IEEE Transactions on Mobile Computing,vol.3,no.4,PP.366-379,October-December 2004
[107]Wang Shu, Liu Kelong.A Distributed Sensor Network Localization Scheme Motivated by Graph Rigidity Theory[J].The Mediterranean Journal of Measurement and Control,2005,(4):185-190